Extending Answer Set Programming

计算问题可以通过为它设计和实现算法来解决，或者通过用直观的建模语言来表示，该语言附带用于计算用该语言编写的表达式的软件。后者是所谓的陈述性问题解决的一个例子。本研究的目的是通过解决两个重要问题：(A)开放世界和封闭世界推理的集成，以及(B)使用回答集编程作为领域和可废止本体知识的统一建模和推理语言，来扩展答案集编程(Asp)，它已经成为解决计算问题的一种很有前途的说明性范例。*ASP的主要思想是将给定的问题表示为约束，这些约束在稳定模型(也称为答案集)语义下以基于规则的语言表示。直观地说，答案集对应于正在解决的问题的解决方案。当计算出答案集时，用户可以从中提取相应的解。多年来，在知识表示、逻辑编程和约束满足等方面的研究已经形成了ASP的提法。其目标是提供一种用于对领域知识进行建模的声明性语言，以及用于处理用这种语言编写的程序的计算工具。随着竞争激烈的ASP解算器已经建立，已经开发了几个应用，例如，在分子生物学、决策支持系统、计划和调度、解决谜题和游戏，以及最近在本体论知识发挥关键作用的Web推理中。*在本研究中，我们提出了两个扩展ASP的方向，每个方向都有各自的特点和优点。一个是关于ASP与类逻辑的可判定片段的紧密集成，其中我们将解决结合开放和封闭世界推理的问题。这类推理任务经常出现在具有复杂异质系统的推理环境中。这种方法的一个优点是它允许采用高效的推理引擎，例如为使用本体进行推理而开发的那些。另一个方向涉及ASP，使用统一语言中的领域和可废止的本体知识来表示和推理存在规则。这将导致不同类型推理的无缝集成，并为构建下一代ASP解算器奠定基础。*很好地预期，许多进一步的应用将需要处理领域知识和本体知识，例如计划和调度、临床试验和复杂的管理系统以及与Web的推理。这项研究的成功将为推动采用ASP解决下一波新应用提供必要的见解和计算机制。*

A computational problem can be solved by designing and implementing an algorithm for it, or by expressing it in an intuitive modelling language that comes with software for evaluating expressions written in the language. The latter is an example of what is called declarative problem solving. The goal of this research is to extend Answer Set Programming (ASP for short), which has emerged as a promising declarative paradigm for solving computational problems, by addressing two important problems: (a) on integrating open-world with closed-world reasoning, and (b) on using ASP as a uniform language for modeling and reasoning with domain as well as defeasible ontological knowledge. ****The main idea of ASP is that a given problem is stated in terms of constraints, which are expressed in a rule-based language under the stable model (also called answer set) semantics. Intuitively, an answer set corresponds to a solution to the problem being solved. When an answer set is computed, the user can extract from it the corresponding solution. The formulation of ASP has been crystallized from years of research in knowledge representation, logic programming, and constraint satisfaction. The goal is to provide a declarative language for modelling domain knowledge and computational tools for processing programs written in such a language. With highly competitive ASP solvers already built, several applications have been developed, for example, in molecular biology, decision support systems, planning and scheduling, solving puzzles and games, and more recently, in reasoning with the Web where ontological knowledge plays a critical role. ****In this research, we propose to study two directions of extending ASP, each with distinguished features and merits. One is on a tight integration of ASP with decidable fragments of class logic, where we will address the problem of combining open and closed world reasoning. These kind of reasoning tasks often arise in the context of reasoning with complex heterogeneous systems. One advantage of this approach is that it allows the adoption of efficient inference engines, e.g., those developed for reasoning with ontology. The other direction concerns ASP with existential rules for representing and reasoning with domain and defeasible ontological knowledge in a uniform language. This will result in a seamless integration of different kinds of reasoning and establish foundations for building the next generation ASP solvers. ***It is well anticipated that many further applications would require processing domain knowledge in conjunction with ontological knowledge, such as planning and scheduling, clinical trial and complex management systems, and reasoning with the Web. The success of this research will provide needed insights, and computational mechanisms, for advancing the ASP adoption in solving the next wave of new applications.******